{"id":"W3165762224","doi":"","title":"INVESTIGATING THE PARAMETERS OF PRE-/POST-CONDITIONING ON HUMAN-DERIVED CANCER CELLS","year":2019,"lang":"en","type":"dissertation","venue":"MacSphere (McMaster University)","topic":"Chemical Reactions and Isotopes","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Bruce Power; McMaster University","keywords":"Conditioning; Cancer; Biology; Cancer research; Computational biology; Medicine; Mathematics; Internal medicine; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001043704,0.0002959481,0.0003413129,0.0001162519,0.0003293125,0.00002584924,0.0004269695,0.000493388,0.04950589],"category_scores_gemma":[0.00002953877,0.0002634707,0.0002014834,0.0002706656,0.0001783779,0.0001123349,0.00005838826,0.00112617,0.00006739034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001520676,"about_ca_system_score_gemma":0.0001354723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004570338,"about_ca_topic_score_gemma":0.0003454887,"domain_scores_codex":[0.9986937,0.000211359,0.0002684724,0.0003680572,0.0001714044,0.0002869816],"domain_scores_gemma":[0.9986667,0.0004120525,0.0004509783,0.0002198447,0.0001417807,0.0001086302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004185928,0.0001645996,0.0008243673,0.0003804187,0.0005940346,0.00001115467,0.003076797,0.002039886,0.8971758,0.0009098902,0.001932596,0.09247184],"study_design_scores_gemma":[0.001429328,0.000220038,0.00217034,0.0004322064,0.0007514548,0.000001256523,0.007558337,0.0003666578,0.8142244,0.00009576911,0.1722304,0.0005197737],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7848307,0.00007671025,0.000004987009,0.00009225189,0.0008426766,0.0004478036,0.0001409182,0.00003671556,0.2135273],"genre_scores_gemma":[0.5982377,0.0001213783,0.00009431371,0.0006269171,0.00009174603,0.0000104124,0.0003636787,0.00004468403,0.4004092],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1868819,"threshold_uncertainty_score":0.9999818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07108236300176524,"score_gpt":0.3580790723583085,"score_spread":0.2869967093565433,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}